class_docstr: High-Fidelity Generative Image Compression (HiFiC) encoder.
  
  Args:
      input_dimensions: shape of the input tensor
      latent_features: number of bottleneck features
class_docstr_tok: ["High-Fidelity","Generative","Image","Compression","(","HiFiC",")","encoder",".","Args",":","input_dimensions",":","shape","of","the","input","tensor","latent_features",":","number","of","bottleneck","features"]
code: def forward(self, x: Tensor) -> Tensor:
      return self.blocks(x)
  
file_path: NeuralCompression/neuralcompression/models/_hific_encoder_decoder.py
imports: from typing import List
  import torch
  import torch.nn
  from torch import Tensor
  from neuralcompression.layers import ChannelNorm2D
label_desc: Use image node_id 2 for calling the HiFiCEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: self